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Ms13017
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Ouvrages de la bibliothèque en indexation Ms13017
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A comparison study on EEG signal classification using Component analysis (PCA, ICA) and Support Vector Machine (SVM) / Hadjer Azli
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Titre : A comparison study on EEG signal classification using Component analysis (PCA, ICA) and Support Vector Machine (SVM) Type de document : texte imprimé Auteurs : Hadjer Azli, Auteur ; Mourad Adnane, Directeur de thèse Editeur : [S.l.] : [s.n.] Année de publication : 2017 Importance : 55 f. Présentation : ill. Format : 30 cm. Accompagnement : 1 CD-ROM. Note générale : Mémoire de Master : Electronique : Alger, Ecole Nationale Polytechnique : 2017
Bibliogr. f. 48 - 49 . Annexes f. 50 - 55Langues : Anglais (eng) Langues originales : Anglais (eng) Mots-clés : Electroencephalogram (EEG)
Discrete Wavelet Transform (DWT)
Independent Component Analysis (ICA)
Principal Component Analysis (PCA)
Support Vector Machine (SVM) Epileptic SeizureIndex. décimale : Ms13017 Résumé : This studyaims to analyze and process Electroencephalogram (EEG)signals using an automated classification method with Support vector machine
(SVM), to categorize patient’s seizure: epileptic or non-epileptic.
We employed a framework of signal analysis techniques, and we started by applying discrete wavelet decomposition(DWT) on the original signal, followed by extracting a set of statistical features and building the feature matrix.
Next, a feature reduction PCA and ICA were explored to represent the data in a new distinct space with reduced dimension.
Finally, an SVM algorithm was trained and used upon a set of testing data to be classified: epileptic or not.
The performance of classification process due to different methods is presented and compared to show the excellent classification process.A comparison study on EEG signal classification using Component analysis (PCA, ICA) and Support Vector Machine (SVM) [texte imprimé] / Hadjer Azli, Auteur ; Mourad Adnane, Directeur de thèse . - [S.l.] : [s.n.], 2017 . - 55 f. : ill. ; 30 cm. + 1 CD-ROM.
Mémoire de Master : Electronique : Alger, Ecole Nationale Polytechnique : 2017
Bibliogr. f. 48 - 49 . Annexes f. 50 - 55
Langues : Anglais (eng) Langues originales : Anglais (eng)
Mots-clés : Electroencephalogram (EEG)
Discrete Wavelet Transform (DWT)
Independent Component Analysis (ICA)
Principal Component Analysis (PCA)
Support Vector Machine (SVM) Epileptic SeizureIndex. décimale : Ms13017 Résumé : This studyaims to analyze and process Electroencephalogram (EEG)signals using an automated classification method with Support vector machine
(SVM), to categorize patient’s seizure: epileptic or non-epileptic.
We employed a framework of signal analysis techniques, and we started by applying discrete wavelet decomposition(DWT) on the original signal, followed by extracting a set of statistical features and building the feature matrix.
Next, a feature reduction PCA and ICA were explored to represent the data in a new distinct space with reduced dimension.
Finally, an SVM algorithm was trained and used upon a set of testing data to be classified: epileptic or not.
The performance of classification process due to different methods is presented and compared to show the excellent classification process.Exemplaires
Code-barres Cote Support Localisation Section Disponibilité Spécialité Etat_Exemplaire S000185 Ms13017 Papier Bibliothèque centrale Mémoire de Master Disponible Metallurgie En bon état Documents numériques
AZLI.Hadjer.pdfURL